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Water quality is an important criterion for evaluating the suitability of water for drinking and domestic purpose. The main objective of this study was to investigate the physicochemical characterization of groundwater for drinking water consumption. Ten captured sources were selected from three aquifers including the Guelma Mio-Plio-Quaternary alluvial basin; the Senonian Heliopolis Neritic limestone aquifer, and the Eocene limestones of Ras El Agba-Sellaoua aquifer. The analyses concerned the periods of high water in May 2017 and low water in August 2017. Twelve parameters were determined for the water samples: pH, T (°C), EC, Ca2+, Mg2+, Na+, K+, Cl−, HCO3−, SO42−, NO3−, TH (hydrotimetric degree), TAT (total alkalinity titration). The interpretation of the various analytical results allowed the determination of the chemical facies and the classification of the groundwater aquifers as follows: (i) in the alluvial layer, the gypsiferous marl substratum and the clays of the three terraces (high, medium and low) have given the water a chlorinated calcium chemical facies in the east part of the study area and travertines feeding partly alluvial layer, and have given a bicarbonated calcium water facies in the west, (ii) in the Senonian of Heliopolis limestone and Eocene carbonate formations of Ras El Agba-Sellaoua, the chemical facies are calcium bicarbonate. Water isotopes (δ18O and δD) helped to determine the origin of groundwater. Overall, the groundwater in the area is hard and has significant to excessive mineralization. It is progressively degraded in the direction of flow, especially in the Guelma alluvial aquifer. [ABSTRACT FROM AUTHOR]

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In this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error ( RMSE), mean absolute error ( MAE), coefficient of efficiency ( CE) and coefficient of determination ( R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Water & Land Development is the property of Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Water & Land Development is the property of Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)